Mendelian Randomization Analysis: Type 2 Diabetes and Alzheimer's Disease
By Dr. Shea Andrews, generated on 2018-08-24
## [1] FALSEData sources
Type 2 Diabetes Xu et al Nature Genet 2018: Here we conduct a meta-analysis of genome-wide association studies (GWAS) with ~16 million genetic variants in 62,892 T2D cases and 596,424 controls of European ancestry by combining 3 GWAS data sets of European ancestry: DIAbetes Genetics Replication and Meta-analysis (DIAGRAM), Genetic Epidemiology Research on Aging (GERA), and the full cohort release of the UK Biobank (UKB). We identify 139 common and 4 rare variants associated with T2D, 42 of which (39 common and 3 rare variants) are independent of the known variants.
Late Onset Alzheimer’s disease (LOAD, Lambert et al 2013): The International Genomics of Alzheimer’s Project (IGAP) is a meta-analysis of 4 previously published GWAS datasets: the European Alzheimer’s Disease Imitative (EADI), the Alzheimer Disease Genetics Consortium (ADGC), Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE), and Genetic and Environmental Risk in AD (GERAD) and includes a sample of 17,008 LOAD cases and 37,154 cognitively normal elder controls. Participants in IGAP were of European ancestry, the average age was 71 and 58.4% of participants were women.
Alzheimer’s Age of Onset Surivial (AAOS, Huang et al 2017): A GWAS of age of onset in LOAD was conducted in 14,406 AD case samples and 25,849 control samples from the IGAP using Cox proportional hazard regressions. Participants were of European ancestry, in cases the the average AAO was 74.8 and 61.7% were women, in controls the average AAE was 79.0 and 59.6% were women.
CSF Ab42, tau & ptau (AB42, ptau, tau, Deming et al 2017): A GWAS of CSF AB42, ptau and tau levels (pg/mL) was conducted in 3,146 participants. Participants were of Eurpean ancestry.
Instrumental Vaibles
LD Clumping: For standard two sample MR it is important to ensure that the instruments for the exposure are independent. LD clumping can be performed using the data_clump function from the TwoSampleMR package, which uses EUR samples from the 1000 genomes project to estimate LD between SNPs and amonst SNPs that have and LD above a given threshold, only the SNP with the lowest p-value will be retained.
Proxy SNPs: SNPs associated with Type 2 Diabetes were extracted from the GWAS of LOAD, AAOS, AB42, ptau and tau. Where SNPs were not available in the outcome GWAS, the EUR thousand genomes was queried to identified potential proxy SNPs that are in linkage disequilibrium (r2 > 0.8) of the missing SNP.
Type 2 Diabetes
Type 2 Diabetes: 14452 SNPs (Table 1) were assoicated with were associated with Type 2 Diabetes at p < 5e-6. After LD clumping, 14012 of 14452 SNPs were removed.
LOAD
Of the the 440 SNPs associated with Type 2 Diabetes, 414 were available in the LOAD GWAS (Table 2).
Table 2: SNPS associated with Type 2 Diabetes avalible in LOAD GWASAAOS
Of the the 440 SNPs associated with Type 2 Diabetes, 440 were available in the AAOS GWAS (Table 3).
Table 3: SNPS associated withAB42
Of the the 440 SNPs associated with Type 2 Diabetes, 382 were available in the CSF AB42 GWAS (Table 4).
Table 4: SNPS associated with Type 2 Diabetes avalible in ab42 GWASPtau
Of the the 440 SNPs associated with Type 2 Diabetes, 413 were available in the CSF Ptau GWAS (Table 5).
Table 5: SNPS associated with Type 2 Diabetes avalible in Ptau GWASTau
Of the the 440 SNPs associated with Type 2 Diabetes, 382 were available in the CSF Tau GWAS (Table 6).
Table 6: SNPS associated with Type 2 Diabetes avalible in tau GWASData harmonization
Harmonize the exposure and outcome datasets so that the effect of a SNP on the exposure and the effect of that SNP on the outcome correspond to the same allele. The harmonise_data function from the TwoSampleMR package can be used to perform the harmonization step, by default it try’s to infer the forward strand alleles using allele frequency information. EAF were not availbe in the IGAP summary statisitics, as such the allele frequencies reported in the AAOS anaylsis were used.
Type 2 Diabetes ~ LOAD
Table 7: Harmonized Type 2 Diabetes and LOAD datasetsType 2 Diabetes ~ AAOS
Table 8: Harmonized Type 2 Diabetes and AAOS datasetsType 2 Diabetes ~ AB42
Table 9: Harmonized Type 2 Diabetes and AB42 datasetsType 2 Diabetes ~ Ptau
Table 10: Harmonized Type 2 Diabetes and Ptau datasetsType 2 Diabetes ~ Tau
Table 11: Harmonized Type 2 Diabetes and Tau datasets
Pleiotropy
Pleiotropy was assesed using Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO), that allows for the evlation of evaluation of horizontal pleiotropy in a standared MR model. MR-PRESSO performs a global test for detection of horizontal pleiotropy and correction of pleiotropy via outlier removal.
Type 2 Diabetes ~ LOAD: The MR-PRESSO global test for pleiotropy was significant (p = <1e-04). The following SNPs were removed due to pleiotropy: rs10410910, rs1063355, rs11039266
Type 2 Diabetes ~ AAOS: The MR-PRESSO global test for pleiotropy was significant (p = <1e-04). The following SNPs were removed due to pleiotropy: No significant outliers
Type 2 Diabetes ~ AB42: The MR-PRESSO global test for pleiotropy was significant (p = 0.0126).. The following SNPs were removed due to pleiotropy: rs77614642
Type 2 Diabetes ~ Ptau: The MR-PRESSO global test for pleiotropy was non-significant.
Type 2 Diabetes ~ Tau: The MR-PRESSO global test for pleiotropy was non-significant..
Mendelian Randomization Analysis
To obtain an overall estimate of causal effect, the SNP-exposure (Major Depressive Disorder) and SNP-outcome coefficients (Alzheimer’s disease and Alzheimer’s Age of Onset) were combined in 1) a random-effects meta-analysis using an inverse-variance weighted approach (IVW); 2) a Weighted Median approach; 3) and Egger Regression. IVW is equivalent to a weighted regression of the SNP-outcome coefficients on the SNP-exposure coefficients with the intercept constrained to zero. This method assumes that all variants are valid instrumental variables based on the Mendelian randomization assumptions. The causal estimate of the IVW analysis expresses the causal increase in the outcome (or log odds of the outcome for a binary outcome) per unit change in the exposure. Weighted median MR allows for 50% of the instrumental variables to be invalid. MR-Egger regression allows all the instrumental variables to be subject to direct effects (i.e. horizontal pleiotropy), with the intercept representing bias in the causal estimate due to pleiotropy and the slope representing the causal estimate.
Type 2 Diabetes ~ LOAD
Figure 1 illustrates the SNP-specific associations with Type 2 Diabetes versus the association between each SNP and risk of LOAD.
Fig. 1: Scatterplot of SNP effects for the association of Trait and LOAD
Figure 2 and Table 1 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Type 2 Diabetes on risk of LOAD.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 2 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the Trait – LOAD causal estimates against their precession
Table 2: Heterogenity tests for Type 2 Diabetes and LOAD
## Warning in mr_heterogeneity(trait_LOAD.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.Type 2 Diabetes ~ AAOS
Figure 4 illustrates the SNP-specific associations with Type 2 Diabetes versus the association between each SNP and AAOS.
Fig. 4: Scatterplot of SNP effects for the association of Trait and AAOS
Figure 5 and Table 4 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Type 2 Diabetes on AAOS.
Fig. 5: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 6 shows a funnel plot to detect pleiotropy and Table 5 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 6: Funnel plot of the traitohol Conumption – AAOS causal estimates against their precession
Table 5: Heterogenity tests for Type 2 Diabetes and AAOS
## Warning in mr_heterogeneity(trait_AAOS.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.Type 2 Diabetes ~ AB42
Figure 1 illustrates the SNP-specific associations with Type 2 Diabetes versus the association between each SNP and AB42.
Fig. 1: Scatterplot of SNP effects for the association of trait and AB42
Figure 2 and Table 7 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Type 2 Diabetes CSF AB42 levels.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 8 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the trait – AB42 causal estimates against their precession
Table 8: Heterogenity tests for Type 2 Diabetes and AB42
## Warning in mr_heterogeneity(trait_AB42.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.Type 2 Diabetes ~ Ptau
Figure 1 illustrates the SNP-specific associations with Type 2 Diabetes versus the association between each SNP and risk of Ptau.
Fig. 1: Scatterplot of SNP effects for the association of trait and Ptau
Figure 2 and Table 9 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Type 2 Diabetes on risk of Ptau.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 10 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the trait – Ptau causal estimates against their precession
Table 10: Heterogenity tests for Type 2 Diabetes and Ptau
## Warning in mr_heterogeneity(trait_Ptau.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.Type 2 Diabetes ~ Tau
Figure 1 illustrates the SNP-specific associations with Type 2 Diabetes versus the association between each SNP and CSF Tau levels.
Fig. 1: Scatterplot of SNP effects for the association of trait and Tau
Figure 2 and Table 12 shows the SNP-specific effects and overall IVW, weighted median and Egger regression causal estimates of genetically predicted Type 2 Diabetes on risk of Tau.
Fig. 2: Forrest plot of Wald ratios and 95% CIs for SNP-specific and overall IVW, Weighted median and Egger associations
Figure 3 shows a funnel plot to detect pleiotropy and Table 13 show the results of Cochrans Q heterogeneity test to assess for the presence of pleiotropy.
Fig. 3: Funnel plot of the trait – Tau causal estimates against their precession
Table 14: Heterogenity tests for Type 2 Diabetes and Tau
## Warning in mr_heterogeneity(trait_Tau.MRdat, method_list =
## c("mr_egger_regression", : Prior to version 0.4.9 there was a bug in
## the IVW Q statistic estimate, leading to a slight underestimation in
## heterogeneity. This has now been resolved.